Crowd counting and monitoring

Abstract

This document discusses, among other things, methods and systems for determining the number of members in a group as well as changes over a period of time. Using an image of the scene, an overlap area is calculated by projecting portions of the image onto spaced apart and parallel planes. A filter correlates the overlap area to the number of members.

Description

STATEMENT OF GOVERNMENT RIGHTS[0001]The invention was made with Government support under agency grant number IIS-0219863 awarded by the National Science Foundation. The Government has certain rights in this invention.TECHNICAL FIELD[0002]This document pertains generally to image processing, and more particularly, but not by way of limitation, to crowd counting and monitoring.BACKGROUND[0003]In traffic control, accurate data as to pedestrian and crowd tracking can be used to improve safety and traffic flow. For example, a light-controlled intersection can be configured to operate automatically depending on the number of pedestrians waiting to cross a roadway.[0004]Determining the number of members in a crowd is a difficult problem. Manual methods are exceedingly costly and are generally too slow. Previous attempts to use a processor have suffered from scalability and accuracy problems.BRIEF DESCRIPTION OF THE DRAWINGS[0005]In the drawings, which are not necessarily drawn to scale, li...

AI Technical Summary

Benefits of technology

[0145]The graph in FIG. 6 shows the counts produced using the bounds based counting method over the lifetime (284 frames) of a group of 11 people. As can be seen, the results are more reliable and less prone to regions of inaccurate estimates. This estimate is also more stable. The minimum lower bound is very close to the ground truth for a short period, which corresponds to

Problems solved by technology

Determining the number of members in a crowd is a difficult problem.

Manual methods are exceedingly costly and are generally too slow.

Previous attempts to use a processor have suffered from scalability and accuracy problems.

The Kalman filter tracker alone does not provide information about the number of people in the group.

It may be insufficient to estimate the count independently frame-wise since such a method is not robust to unusual situations which may occur temporarily.

Since the shape of the blob does not represent a single group, the assumptions about the shape of the group do not hold true.

The heuristic-based method may not be suitable for all situations.

For example, the heuristic-based method assumes a high spatial density wh

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